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A Study on Relationship between Data Mining and Big Data

Kavita Srivastava1

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 451-452, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.451452

Online published on Feb 28, 2019

Copyright © Kavita Srivastava . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kavita Srivastava, “A Study on Relationship between Data Mining and Big Data,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.451-452, 2019.

MLA Style Citation: Kavita Srivastava "A Study on Relationship between Data Mining and Big Data." International Journal of Computer Sciences and Engineering 7.2 (2019): 451-452.

APA Style Citation: Kavita Srivastava, (2019). A Study on Relationship between Data Mining and Big Data. International Journal of Computer Sciences and Engineering, 7(2), 451-452.

BibTex Style Citation:
@article{Srivastava_2019,
author = {Kavita Srivastava},
title = {A Study on Relationship between Data Mining and Big Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {451-452},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3685},
doi = {https://doi.org/10.26438/ijcse/v7i2.451452}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.451452}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3685
TI - A Study on Relationship between Data Mining and Big Data
T2 - International Journal of Computer Sciences and Engineering
AU - Kavita Srivastava
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 451-452
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

Big data is an expression for a data set. Big data sets are those that exceed the straightforward sort of database and data taking care of models that were utilized in before times, when big data was progressively costly and less achievable. Experts also initiate the distinctiveness and function of several popular running platforms. In this paper, we elaborate to identify the challenges and issues of big data and data Ming with closed relationship. We recognized quite a lot of factors from the big data and data Ming perspective and we also decorated the data Ming issue that justify considerable additional research and development. However, database and data taking care of models issues there a crucial difficulty for user to get used to into data Mining.

Key-Words / Index Term

Mining, Architecture, Challenges, Big Data, Research Issues

References

[1]. K. Su, H. Huang, X. Wu, and S. Zhang, “A Logical Framework for Identifying Quality Knowledge from Different Data Sources,” Decision Support Systems, vol. 42, no. 3, pp. 1673-1683, 2006.
[2]. E.Y. Chang, H. Bai, and K. Zhu, “Parallel Algorithms for Mining Large-Scale Rich-Media Data,” Proc. 17th ACM Int’l Conf. Multimedia, (MM ’09,) pp. 917-918, 2009.
[3]. D. Howe et al., “Big Data: The Future of Biocuration,” Nature, vol. 455, pp. 47-50, Sept. 2008.
[4]. A. Labrinidis and H. Jagadish, “Challenges and Opportunities with Big Data,” Proc. VLDB Endowment, vol. 5, no. 12, 2032-2033, 2012.
[5]. Chen et al. 2004, R. Chen, K. Sivakumar, and H. Kargupta, Collective Mining of Bayesian Networks from Distributed Heterogeneous Data, Knowledge and Information Systems, 6(2):164-187, 2004.
[6]. Chen et al. 2012, Yi-Cheng Chen, Wen-Chih Peng, Suh-Yin Lee, Efficient algorithms for influence maximization in social networks, Knowledge and Information Systems, December 2012, Volume 33, Issue 3, pp 577-601
[7]. Chu et al., 2006, Chu C.T., Kim S.K., Lin Y.A., Yu Y., Bradski G.R., Ng A.Y., Olukotun K., Map- reduce for machine learning on multicore, In: Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS `06), MIT Press, 2006, pp. 281-288.
[8]. Cormode G. and Srivastava D. 2009, Anonymized Data: Generation, Models, Usage, in Proc. of SIGMOD, 2009. pp. 1015-1018.
[9]. Brijesh Kumar Bhardwaj, “Performance Analysis and Evaluation in Data Mining: An Educational Perspective”, International Journal of Scientific Research and Reviews, Vol. 7, Issue 4, July-Sept. 2018.
[10]. Anshul Mishra, Devendra Agarwal and M. H. Khan, “Availability Estimation Model: Fault Perspective”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 6, June 2017.